Redistribution effects of water tariffs

Nguyen Bich Ngoc, Jacques Teller 13 January 2022

components mean sd X0. X25. X50. X75. X100.
fixedpc fixedpc 42.2477053 21.1582293 8.4256732 25.3063671 36.5688283 56.9366554 89.1224984
volpc volpc 57.4800075 21.1322581 10.7675390 42.7946796 63.1426726 74.4054907 91.2801227
fsapc fsapc 0.2722872 0.0348113 0.1055832 0.2732136 0.2825452 0.2883638 0.3154574
## Warning: Removed 150 row(s) containing missing values (geom_path).

## Warning: Removed 150 row(s) containing missing values (geom_path).

## Warning: Removed 192 row(s) containing missing values (geom_path).

summary(df$TEH)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.2122  0.7290  1.0389  1.2432  1.5183  9.0135
sum(df$TEH > 3) * 100 / nrow(df)
## [1] 3.645833
## 3.2 by income quantiles -------

### income quantiles -------
Household income quintile characteristics
Quintile Number of households Average household size Min income (EUR/month) Max income (EUR/month)
1 346 1.59 125 1750
2 346 2.01 1750 2250
3 346 2.38 2250 2750
4 345 2.80 2750 3750
5 345 3.35 3750 5250

Income per equivalent adults for different household income group

# Correlation between water consumption and household income should use spearman?????

cor.test(df$csmptv, df$income, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$income
## t = 15.729, df = 1726, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3121384 0.3946480
## sample estimates:
##      cor 
## 0.354082
cor.test(df$csmptv, df$income, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$income, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$income
## S = 536353649, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3763062
# Correlation between water consumption and income per equivalent adult should use spearman?????

cor.test(df$csmptv, df$inceqa, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$inceqa
## t = 1.4269, df = 1726, p-value = 0.1538
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01285165  0.08134826
## sample estimates:
##        cor 
## 0.03432454
cor.test(df$csmptv, df$inceqa, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$inceqa, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$inceqa
## S = 803896839, p-value = 0.006707
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.06519613

incqnt fixedpc_fn1 volpc_fn1 fsapc_fn1 fixedpc_fn2 volpc_fn2 fsapc_fn2 fixed vol fsa
1 54.70 45.04 0.25 21.83 21.79 0.048 54.70±21.83 45.04±21.79 0.25±0.048
2 46.74 52.99 0.27 20.81 20.78 0.035 46.74±20.81 52.99±20.78 0.27±0.035
3 39.87 59.85 0.28 19.26 19.24 0.029 39.87±19.26 59.85±19.24 0.28±0.029
4 36.94 62.78 0.28 18.04 18.02 0.022 36.94±18.04 62.78±18.02 0.28±0.022
5 32.95 66.77 0.28 18.47 18.44 0.028 32.95±18.47 66.77±18.44 0.28±0.028
incqnt tehprop
1 12.7167630
2 3.7572254
3 1.4450867
4 0.2898551
5 0.0000000
## Warning: Graphs cannot be horizontally aligned unless the axis parameter
## is set. Placing graphs unaligned.

summary(df$avrprc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.200   4.595   4.690   5.000   4.850  12.549
summary(df$avrprc[df$inccat == "precarious"])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   4.222   4.605   4.734   5.152   4.915  10.592       1
summary(df$subs[df$inccat == "precarious"])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## -68.1035  -4.7756   0.5658  -4.0094   8.3744  27.4910        1
summary(df$mgnprc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.158   4.458   4.458   3.982   4.458   4.658
summary(df$mgnprc[df$inccat == "precarious"])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.158   4.153   4.458   3.754   4.458   4.658       1
## 3.5. changing fixed  -----

### new cvd ------
CVD_SWDE CVD_CILE CVD_inBW CVA scenario fixed rwtt mgpr_bl1 mgpr_bl2
2.4480 2.6366 2.1600 1.745 As in 2014 101.438 0 1.2272 4.1994
4.2744 4.5442 3.6839 1.745 1 0.000 0 2.1344 6.0138
3.3730 3.6365 2.8864 1.745 2 50.000 0 1.6875 5.1200
2.4716 2.7289 2.0890 1.745 3 100.000 0 1.2406 4.2262
1.5702 1.8212 1.2916 1.745 4 150.000 0 0.7937 3.3324
0.6688 0.9135 0.4942 1.745 5 200.000 0 0.3468 2.4386

CVD_SWDE CVD_CILE CVD_inBW CVA scenario fixed rwtt mgpr_bl1 mgpr_bl2
2.4480 2.6366 2.1600 1.745 6 101.4380 0 1.2272 4.1994
2.1517 2.4683 1.8852 1.745 7 95.9578 50 1.0902 3.9254
1.8554 2.3000 1.6104 1.745 8 90.4777 100 0.9532 3.6514
1.5591 2.1318 1.3356 1.745 9 84.9975 150 0.8162 3.3774
1.2628 1.9635 1.0608 1.745 10 79.5174 200 0.6792 3.1034

bl1_SWDE bl1_CILE bl1_inBW fixed revincr mgpr_bl1 mgpr_bl2
4.427231 4.635599 4.069370 0 0.0 4.431018 4.431018
3.710676 3.914732 3.423071 50 0.0 3.719586 3.719586
2.994121 3.193866 2.776773 100 0.0 3.008154 3.008154
4.871204 5.100408 4.477557 0 0.1 4.875369 4.875369
4.154649 4.379542 3.831259 50 0.1 4.163938 4.163938
3.438094 3.658676 3.184960 100 0.1 3.452506 3.452506
5.315177 5.565218 4.885744 0 0.2 5.319721 5.319721
4.598622 4.844352 4.239446 50 0.2 4.608289 4.608289
3.882067 4.123486 3.593147 100 0.2 3.896857 3.896857
bl1_SWDE bl1_CILE bl1_inBW fixed revincr mgpr_bl1 mgpr_bl2
1.789001 1.876093 1.594286 0 0.0 1.786848 6.253969
1.499448 1.584348 1.341081 50 0.0 1.499952 5.249832
1.209895 1.292603 1.087876 100 0.0 1.213056 4.245695
1.968406 2.064208 1.754204 0 0.1 1.966037 6.881130
1.678853 1.772463 1.500999 50 0.1 1.679141 5.876993
1.389300 1.480718 1.247794 100 0.1 1.392245 4.872856
2.147811 2.252323 1.914122 0 0.2 2.145226 7.508291
1.858259 1.960578 1.660917 50 0.2 1.858330 6.504154
1.568706 1.668834 1.407712 100 0.2 1.571433 5.500017
bl1_SWDE bl1_CILE bl1_inBW fixed revincr mgpr_bl1 mgpr_bl2
1.816621 1.870233 1.608886 0 0.0 1.808024 6.328085
1.522598 1.579399 1.353362 50 0.0 1.517698 5.311944
1.228575 1.288566 1.097838 100 0.0 1.227372 4.295803
1.998797 2.057760 1.770269 0 0.1 1.989337 6.962678
1.704773 1.766927 1.514745 50 0.1 1.699011 5.946537
1.410750 1.476093 1.259221 100 0.1 1.408685 4.930397
2.180972 2.245288 1.931651 0 0.2 2.170649 7.597272
1.886948 1.954454 1.676128 50 0.2 1.880323 6.581131
1.592925 1.663621 1.420604 100 0.2 1.589997 5.564990